I Eat Healthier Than You: Differences in Healthy and ... · I Eat Healthier Than You: Differences...

Nutrients 2015, 7, 4638-4660; doi:10.3390/nu7064638

nutrients ISSN 2072-6643

www.mdpi.com/journal/nutrients

Article

I Eat Healthier Than You: Differences in Healthy and Unhealthy Food Choices for Oneself and for Others

Gudrun Sproesser 1,*, Verena Kohlbrenner 1, Harald Schupp 2 and Britta Renner 1

1 Psychological Assessment and Health Psychology, University of Konstanz, P.O. Box 47,

Konstanz 78457, Germany; E-Mails: [email protected] (V.K.);

[email protected] (B.R.) 2 General Psychology, University of Konstanz, P.O. Box 36, Konstanz 78457, Germany;

E-Mail: [email protected]

* Author to whom correspondence should be addressed; E-Mail: [email protected];

Tel.: +49-7531-88-2623; Fax: +49-7531-88-5226.

Received: 21 April 2015 / Accepted: 1 June 2015 / Published: 9 June 2015

Abstract: The present study investigated self-other biases in actual eating behavior based

on the observation of three different eating situations. To capture the complexity of real

life food choices within a well-controlled setting, an ecologically valid fake food buffet

with 72 different foods was employed. Sixty participants chose a healthy, a typical, and an

unhealthy meal for themselves and for an average peer. We found that the typical meal for

the self was more similar to the healthy than to the unhealthy meal in terms of energy

content: The mean difference between the typical and healthy meals was MΔ = 1368 kJ

(327 kcal) as compared to a mean difference between the typical and unhealthy meals of

MΔ = 3075 kJ (735 kcal). Moreover, there was evidence that people apply asymmetrical

standards for themselves and others: Participants chose more energy for a peer than for

themselves (M = 4983 kJ or 1191 kcal on average for the peers’ meals vs. M = 3929 kJ or

939 kcal on average for the own meals) and more high-caloric food items for a typical

meal, indicating a self-other bias. This comparatively positive self-view is in stark contrast

to epidemiological data indicating overall unhealthy eating habits and demands further

examination of its consequences for behavior change.

Keywords: fake food buffet; food choice; self-other bias; optimism in eating behavior

OPEN ACCESS

Nutrients 2015, 7 4639

1. Introduction

Headlines stating that people have unhealthy eating habits such as “The way America eats is

killing us” in September 2013 [1] are not surprising. Most people, experts as well as laypeople, seem

to agree that eating habits in the general population are unhealthy, with too many calories and too

many high-caloric foods.

However, when asking people how they gauge their own eating habits, a contrasting picture

emerges. As in other life domains, many people tend to believe that they behave more positively than

their average peers. For example, Sparks and colleagues [2] reported that respondents believed on

average that they eat less fat and sweets in relation to others (see also [3]). In a similar vein, in a

community sample, we found that only 9% believed that their own eating was less healthy than their

peers’ eating, whereas 54% rated their eating as healthier than their peers’ eating [4]. This positive

view of one’s own eating habits stands in contrast to epidemiological data which indicates that many

people in western societies eat too much as well as too many high-caloric foods and do not eat enough

fruits and vegetables. This raises the question of why we think that others eat more unhealthily than

we do.

1.1. Self-Other Biases

The phenomenon that people think more positively of themselves than others, often to an unrealistic

degree, has been demonstrated extensively (see [5–8] for reviews). This self-favoring difference in

self- and others-perception has been named as “unrealistic optimism” [9], “false uniqueness” [10],

or the “holier than thou” effect [11], depending on the judgment domain. Importantly, flattering

self-assessments can stem from three different mechanisms. First, people might perceive themselves

overly optimistically but others rather accurately (self-bias). Second, people might perceive themselves

relatively accurately but others too pessimistically (peer-bias). Third, people might perceive both

themselves and others inaccurately. Accordingly, to disentangle these three possible types of

misjudgment, self- and others-views need to be assessed separately [5,7,8]. Whereas numerous studies

have assessed self and peer related views in various judgment domains (e.g., [12]), to our knowledge,

no study has yet assessed self- and peer-views separately in respect to eating. Hence, although people

clearly demonstrate a self-flattering bias at the group level when they claim that they eat more

healthily than their peers on average, evidence to identify whether the self-view, peer-view, or both is

the source of this bias is lacking.

There are numerous psychological mechanisms underlying self-flattering biases (e.g., [5,8,13]). In

particular, general or broad personality traits and behaviors typically elicit a stronger self-favoring bias

than those which are more concrete and whose definition is more constrained [5]. In a similar vein,

people are typically asked about hypothetical habits and behaviors, that is, how they and their peers

generally behave or would behave in a given situation (see [11], see also [14]). As this represents a

rather ambiguous judgment task, people might either lack the necessary information to provide

accurate assessments or fail to integrate and weigh all relevant and available information [5].

Actual behavior observation reduces the ambiguity of the judgment task for participants and could,

thereby, provide a more accurate picture of self-other biases than hypothetical or self-report assessment


methods. Interestingly, in studies in which predicted and actual behavior was observed, people were

typically more accurate in predicting others’ behaviors than in predicting their own behaviors,

indicating a self-bias ([11,14], for review see [15]). For example, Epley and Dunning [11] showed that

83% of their sample predicted that they would buy a flower at a charity event, whereas they predicted

that 56% of their peers would do so. After the charity event, 43% of the participants had actually

bought a flower. Since the average prediction for the self was more discrepant from the observed

behavior than the prediction for the peers, Epley and Dunning [11] concluded that participants showed

a greater self- than peer-bias (see also [14]). However, one could argue that the observed self-bias is

mainly due to poor prediction abilities for the self rather than biased perceptions of actual behavior.

Recent studies suggest that people base their predictions on “ideal” behavior and, for example, attach

too much weight to current intentions, whereas they fail to make realistic adjustments for situational

constraints on intended actions [16].

In order to disentangle prediction errors from self-flattering perceptions of one’s own actual

behavior as compared to others, we need to elicit separate behavioral probes for the self and for others.

However, in current study designs this is not possible since, for a single observed behavior

(e.g., likelihood of charity donation), average peer-behavior (e.g., that 43% of the sample made a

donation) is an aggregate of self-behaviors and, therefore, equal to the average self-behavior. This

raises the question, how can we measure self-other biases in actual behavior?

1.2. Measuring Self-Other Biases in Actual Eating Behaviors: Probing Behavioral Standards for the

Self and an Average Peer

A first challenge in measuring self-other biases in actual behavior is to observe behaviors that are

representative of what people do in their everyday lives [5,17]. Whereas natural environments have the

advantage of ecological validity, a disadvantage is that food choice can be confounded by, for

example, context variables such as food variety or lighting (e.g., [18]; see [19] for an overview).

In contrast, laboratories offer well-controlled settings [20]. However, laboratory research faces the

challenge of being representative of real-life situations; as Brunswik [21] put it, it has to have a

representative design (see also [22]). Simulating the diversity of available food choices in real life in a

standardized way is methodologically challenging and often conflicts with practical issues, for

example, high costs, limited infrastructure, high preparation effort, and waste. To overcome these

problems, an innovative method was recently developed: using replica food items [23]. With fake food

items, real life, such as eating in buffet style restaurants, can be simulated under well-controlled

conditions. This method has high ecological validity, has been shown to be reliable and a valid

assessment of real food choices [23]. Furthermore, it allows capturing the complexity of food choice

behavior that consumers face. On the one hand, how much people eat, that is the total energy content,

is central for energy balance and weight maintenance (e.g., [24]). On the other hand, eating behavior

comprises more than just consuming energy as an incredibly large variety of food items is available in

the western world. With the fake food method, both variety and quantity can be assessed by food

profiles and total energy content.

A second great challenge in measuring self-other biases in actual behavior is to separate behavior

observations for the two different targets: the self and others. We would like to suggest a new method


for assessing self-other biases in actual eating behavior based on the observation of three different

eating situations: a typical, a healthy, and an unhealthy meal. Food choices in these three situations

give information about a range of prototypical behaviors and, thus, give information which standards

people apply, on average, for themselves and others in the different situations. Selected meals are a

proxy for meals that are regarded as exemplary for the self and others in these situations. Hence,

typical, healthy, and unhealthy behavioral standards for the self and others can be assessed.

Using a fake food buffet as standardized eating situation, it can be observed which food choices

people make when they choose a typical, a healthy, or an unhealthy meal for themselves and for an

average peer. These choices for the self versus for others enable a comparison between the average

self-behavior, that is the “actual” average behavior profile (aggregated food choices for the self), and

the average peer-behavior, that is the “assumed” average behavior profile (aggregated food choices for

the average peer). The crucial question is whether people use different standards within and between

the three different eating situations as a function of the target consumer (self vs. average peer). Hence,

the selection of a different amount of food or other food items for the peer than for the self within one

eating situation (e.g., typical meal) would indicate a self-other difference in eating standards. However,

if people systematically choose more food (i.e., energy), or different food (e.g., more unhealthy food

items) for the peer than for the self across the three different eating situations, this would provide clear

evidence of asymmetrical behavioral standards for the self and others and thereby a self-other bias in

actual behavior.

1.3. The Present Study

The goals of the present study were twofold: (a) to investigate behavioral standards within the self

and within the peer as target consumers and (b) to examine whether asymmetrical behavioral standards

are employed for the self as compared to an average peer. To this end, behavioral standards for three

different eating situations were assessed: a typical, a healthy, and an unhealthy meal. In addition,

participants were asked to compose these three different meals for two different target consumers: for

themselves and for an average peer. The present study employed a fake food buffet to simulate the

diversity of available food choices in real life within a well-controlled experimental setting [23]. It was

observed both how much people chose (total energy content) and what they chose (food item profiles).

2. Method

2.1. Participants

Sixty participants from the University of Konstanz took part in the study. Of these, 49 (82%) were

female. Participants had a mean age of 22.4 years (SD = 5.5, ranging from 19 to 58 years) and a mean

body mass index (BMI) of 22.1 kg/m2 (SD = 2.6, ranging from 17.9 to 30.0 kg/m2). Participants were

recruited via flyers and received €8 for their participation.

All participants gave written informed consent prior to the collection of data. The study was

conducted according to the guidelines of the Declaration of Helsinki. The procedures were performed

in compliance with relevant laws and institutional guidelines. We strictly followed the German

Psychological Society’s (Deutsche Gesellschaft für Psychologie) guidelines for conducting


psychological studies [25] (see paragraph C.III). These are similar to those of the American

Psychological Association. Moreover, the study was reviewed by the Ethics Committee (Institutional

Review Board) of the University of Konstanz, Baden-Württemberg, Germany (project identification

code: IRB15KN007ML; 28 May 2015). The study was judged to be exempt from full Institutional

Review Board review as no threats to human health, well-being, or dignity were identified.

2.2. Material

In order to investigate participants’ food choices, a buffet with replica food items was prepared

(see Figure 1). The composition of the fake food buffet aimed to represent a typical lunch buffet.

An exploratory pilot study was conducted. Six participants were instructed to put together a lunch meal

for themselves and for an average person of the same age and gender, that is their peer, from a fake

food buffet with 55 different food items (see [26]). Participants were questioned how typical the buffet

was and which items were missing or overrepresented. Overall, they found the buffet to be realistic, to

include a large selection of foods, and found it easy to choose meals. However, they suggested adding

sauces, dessert mousses (e.g., pudding, quark), and a larger selection of salad items. In line with their

remarks, the composition of the buffet was adapted. The final fake food buffet contained a salad

buffet, a selection of main courses and side dishes, a dessert buffet, and drinks. In total, it included

72 different food items (see Figure 1 and Table A1, Appendix).

For food selection, large plates (27 cm diameter), small plates (20 cm diameter), bowls (250 mL

capacity), and trays (38 × 58 cm) were provided. All replica food items were manufactured by Döring

GmbH, Munich, Germany. Small packets of real food condiments were also offered: Salt, pepper,

mayonnaise, ketchup, and mustard. To estimate the energy content of the replica foods, the conversion

factors for the weight of fake food items to corresponding real food items were calculated. Weight data

was either derived from Bucher and colleagues [26] or gained by preparing corresponding real

food items and taking weight measures (see Table A1, Appendix). The estimation of nutrients was based

on the national database for nutritional information in Germany (Bundeslebensmittelschlüssel, [27]).

The amount of energy per meal was established by adding the caloric values of all food items

selected for each meal. Additionally, to assess the quality of food choices, all food items were

categorized based on a qualitative food hierarchy concerning the four food groups plant foods, animal

foods, oils and fats, and drinks (German Nutrition Society, [28]; see also [29]). Within these four

groups, foods were categorized as healthy, neutral, or unhealthy. The healthy food category included

foods with low energy and high nutrient density as well as the recommendation to consume in high

quantities. The neutral food category included foods with medium energy and medium nutrient

density. The unhealthy food category included foods with high energy and low nutrient density as well

as the recommendation to be consumed in small quantities. In total, 36 food items (51%) were

assigned to the healthy food category, 12 food items (17%) to the neutral food category, and 22 food

items (31%) to the unhealthy food category. Salt and pepper were excluded from the assignment as

they were classified as spices.


Figure 1. Fake food buffet containing 72 different food items.


2.3. Procedure

Participants were invited to the laboratory individually. In order to create standardized internal

states of hunger, they were asked not to eat and to drink only water for two hours before participation.

After giving informed consent, participants filled in a first questionnaire on demographic data. During

this stage, the fake food buffet was hidden behind a movable wall to avoid potential distractions

and influences.

Next, participants were instructed to compose different lunch meals from the fake food buffet. In

total, participants composed six meals in two serving rounds. One serving round either consisted of

successively choosing three meals for oneself (target: self) or three meals for an average peer of the

same age and gender (target: peer). Within the serving rounds, participants were asked to compose a

healthy meal (meal type: healthy), a meal they would typically compose (meal type: typical), and an

unhealthy meal (meal type: unhealthy). Exact instructions for the typical meal type were as follows:

“Please choose a lunch meal as you would typically choose for yourself in such a buffet situation.”

(target: self)

“Please choose a lunch meal as an average person of your age and gender would typically choose in

such a buffet situation.” (target: peer)

For the healthy and unhealthy meal type, instructions were the same except for replacing “a lunch

meal as … would typically choose in such a buffet situation” with “a healthy/unhealthy lunch meal”.

In order to control for possible carry-over and order effects, the order of choices was alternated

starting either with the self- or the peer-condition. Within the self- and peer-condition, the order of

meal types was alternated, too. However, the same order of meal types was applied in the two target

conditions. Participants were randomly assigned to the order of choices.

After each meal was completed, the experimenter photographed the tray and weighed each food

component or counted the single food items. Between the serving rounds, the buffet was refilled to

initial levels, and the participant filled in a brief questionnaire on food habits. Afterwards, height and

weight were measured following standardized procedures. Participants wore light indoor clothing.

Height was measured without shoes to the nearest 0.1 cm using a wall-mounted stadiometer. Weight

was measured using a digital scale (Omron Body Composition Monitor, BF511, Omron, Hoofddorp,

The Netherland) to the nearest 0.1 kg. Finally, participants were carefully debriefed.

2.4. Statistical Analysis

Statistical analysis was performed using IBM SPSS statistics software, version 22.0 (IBM

Corporation, Armonk, NY, USA). In order to test the research questions, ANOVAs including simple

main effects and simple contrasts were computed. p-Values for non-orthogonal contrasts were

Bonferroni-corrected. In case of violation of the assumption of sphericity, degrees of freedom were

corrected using Greenhouse-Geisser estimates. All tests were based on a 0.05 significance level.

Concerning the total amount of energy, dependent variables were (1) the total amount of energy per

meal and (2) the difference between the energy content of the typical meal minus the energy content of

the healthy meal as well as the difference between the energy content of the unhealthy meal minus the

energy content of the typical meal.


Concerning the quality of food choices, the data set was restructured, treating the 70 food items

included in the healthy, neutral, and unhealthy categories as single observations and the number of

participants who chose each food item within each of the six meals as continuous variable

(see Figure 3). Dependent variables for these analyses were (1) the total number of participants who

chose each food item and (2) the difference between the number of participants who chose each food

item for the typical meal minus for the healthy meal and for the typical minus for the unhealthy meal.

To ensure that the mean of each difference was positive, the difference score typical-healthy was

multiplied by −1 for healthy food items and the difference score typical-unhealthy was multiplied by

−1 for unhealthy food items.

Extreme values concerning the amount of energy chosen from the fake food buffet and the total

number of participants who chose each food item were detected by interquartile range measures. These

extreme values rendered some variables as severely skewed. After excluding extreme values, the

skewness of dependent variables was acceptable (|skewness| < 1.73; |kurtosis| < 3.24; [30]). To secure

our results, all analyses were repeated excluding extreme values. These analyses confirmed results

reported below.

3. Results

3.1. Behavioral Standards for the Self and Others: How Much Did Participants Choose?

A 3 × 2 ANOVA with meal type (healthy vs. typical vs. unhealthy) and target (self vs. peer) as

within-subject factors, and the total amount of energy as a dependent variable revealed significant

main effects of meal type and target which were qualified by a significant meal type × target

interaction (see Table 1). To follow up the interaction, additional analyses were conducted for the

two factors.

Effects of meal type: Behavioral standards for the self. All simple contrasts of meal type within the

self were significant (see Table 1). As shown in Figure 2, participants chose for themselves 3929 kJ

(SE = 207; 939 kcal, SE = 49.5) for a typical meal, 2561 kJ (SE = 166; 612 kcal, SE = 39.6) for the

healthy meal, and 7004 kJ (SE = 373; 1674 kcal, SE = 89.2) for the unhealthy meal. Thus, participants

set different behavioral standards across the three different meal types when choosing for themselves.

Effects of meal type: Behavioral standards for the peer. As with the meals for the self, all simple

contrasts of meal type were significant for the peer (see Table 1). For an average peer, participants

chose 4983 kJ (SE = 269; 1191 kcal, SE = 64.2) for a typical meal, 2824 kJ (SE = 150; 675 kcal,

SE = 35.8) for a healthy meal, and 8238 kJ (SE = 460; 1969 kcal, SE = 110.0) for an unhealthy meal.

Thus, participants also set different behavioral standards across the three different meal types when

choosing for an average peer.


Figure 2. Amount of energy chosen from the fake food buffet as a function of meal type

and target. Error bars indicate standard errors of the mean. *** p < 0.001, ** p < 0.01.

Effects of the target person: Comparing behavioral standards for the self and peer. Interestingly,

systematic differences were seen when comparing choices between the self and peer within the three

eating situations. For both the typical and unhealthy meal, participants chose significantly more energy

for an average peer than for themselves (see Figure 2). However, for the healthy meal type, the amount

of energy chosen was similar.

The similarity of the typical to the healthy and unhealthy meal: A difference score analysis.

For further comparison of the behavioral standards for the self and peer, a 2 × 2 ANOVA with the

meal difference score (typical-healthy vs. unhealthy-typical) and target (self vs. peer) as within-subject

factors was computed (see Table 1). This revealed significant main effects of meal discrepancy and

target. Specifically, within the self, the discrepancy between the typical and healthy meal was

significantly smaller than the discrepancy between the typical and unhealthy meal, MΔ = 1368 kJ;

SE = 165 vs. MΔ = 3075 kJ; SE = 326 (MΔ = 327 kcal; SE = 39.5 vs. MΔ = 735 kcal; SE = 78.0). Hence,

participants’ typical behavioral standard was more similar to their healthy behavioral standard than to

their unhealthy behavioral standard. Simple main effect analyses within the peers’ meals showed a

similar pattern to the own meals: The difference between the typical and the healthy meal was

significantly smaller than the difference between the typical and the unhealthy meal, MΔ = 2155 kJ;

SE = 229 vs. MΔ = 3255 kJ; SE = 396 (MΔ = 515 kcal; SE = 54.8 vs. MΔ = 778 kcal; SE = 94.7).

However, comparing the two targets showed a significant self-other discrepancy: When participants

chose for themselves, the difference between the typical and the healthy meal was significantly smaller

than when they chose for their peer. The difference between the typical and the unhealthy meal did not

differ when participants chose for themselves versus for their peer.


Table 1. Results of the ANOVAS with total amount of energy chosen from the buffet as

dependent variable.

Effect df Effect df Error F p η2p

3 × 2 ANOVA with the factors meal type (healthy vs. typical vs. unhealthy) and target (self vs. peer)

Main effect meal type 1.38 81.34 181.36 < 0.001 0.76

Main effect target 1 59 28.28 < 0.001 0.32

Meal type × target interaction 1.59 93.87 3.85 0.034 0.06

Simple contrasts within self

Healthy vs. typical meal 1 59 68.63 < 0.001 0.54

Typical vs. unhealthy meal 1 59 88.69 < 0.001 0.60

Healthy vs. unhealthy meal 1 59 170.25 < 0.001 0.74

Simple contrasts within peer




Simple main effects target

Healthy meal 1 59 2.35 0.131 -

Typical meal 1 59 24.65 < 0.001 0.30

Unhealthy meal 1 59 10.95 0.002 0.16

2 × 2 ANOVA with the factors meal discrepancy (typical-healthy vs. unhealthy-typical) and target (self vs. peer)

Main effect meal discrepancy 1 59 15.87 < 0.001 0.21

Main effect target 1 59 5.66 0.021 0.09

Meal discrepancy × target interaction 1 59 1.12 0.294 -

Simple main effects meal discrepancy

Self 1 59 19.14 < 0.001 0.25

Peer 1 59 4.64 0.035 0.07


Typical-healthy meal 1 59 9.01 0.004 0.13

Unhealthy-typical meal 1 59 0.18 0.672 -

3.2. Behavioral Standards for the Self and Others: What Did Participants Choose?

A 3 × 2 × 3 ANOVA with the within-subject factors meal type (healthy vs. typical vs. unhealthy)

and target (self vs. peer), the between-subject factor food category (healthy vs. neutral vs. unhealthy),

and the number of participants who chose the food items as dependent variable revealed a significant

meal type × food category interaction, target × food category interaction, and a significant three-way

interaction (see Table 2). Effects of meal type and target were investigated in follow-up analyses.

Effects of meal type: Behavioral standards for the self. As shown in Figure 3, within the own meals,

significantly more participants chose healthy food items for their healthy meal (M = 22; SE = 2.0)

as compared to their typical meal (M = 19; SE = 2.0) and as compared to their unhealthy meal (M = 4;

SE = 1.3). Moreover, significantly more participants chose healthy food items for their typical meal as

compared to their unhealthy meal. For instance, 53 participants chose lettuce for their healthy meal,

48 for their typical meal, and 11 for their unhealthy meal. This pattern was reversed for the unhealthy

food items: These were significantly less often chosen for participants’ healthy meal (M = 2; SE = 2.6)

as compared to their typical meal (M = 7; SE = 2.6) and as compared to their unhealthy meal (M = 16;


SE = 1.7). They were also significantly less often chosen for participants’ typical meals as compared to

their unhealthy meals. In contrast, neutral food items were equally often chosen for participants’

healthy, typical, and unhealthy meals.

Figure 3. (A) Number of participants who chose the single food items for their own

healthy, typical, and unhealthy meals (actual average behavior profile); (B) Number of

participants who chose the single food items for the peers’ healthy, typical, and unhealthy

meal (assumed average behavior profile).

Effects of meal type: Behavioral standards for the peer. Inspecting simple contrasts revealed the

same effects for the peers’ meals as for the own meals: Significantly more participants chose healthy

food items for the peers’ healthy meal (M = 23; SE = 2.1) as compared to the peers’ typical meal

(M = 15; SE = 2.2), and as compared to the peers’ unhealthy meal (M = 2; SE = 1.7). Moreover,

significantly more participants chose healthy food items for the peers’ typical meal as compared to the

peers’ unhealthy meal. This pattern was reversed for the unhealthy food items: These were chosen

significantly less often for the peers’ healthy meal (M = 2; SE = 2.7) as compared to the peers’ typical

meal (M = 9; SE = 2. 8) and as compared to the peers’ unhealthy meal (M = 20; SE = 2.2) as well as

chosen less often for the peers’ typical meal as compared to the peers’ unhealthy meal. In contrast,

neutral food items were chosen equally often for the peers’ healthy, typical, and unhealthy meals.

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Self Healthy Meal Type

Self Typical Meal Type

Self Unhealthy Meal Type

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Peer Healthy Meal Type

Peer Typical Meal Type

Peer Unhealthy Meal Type

A) Self B) Peer

Un

hea

lth

y F

oo

d It

ems

Neu

tral

Fo

od

Item

sH

ealt

hy

Fo

od

Item

s

Tomato sliceLettuce

WaterPepper slice

CarrotsStrawberryCauliflower

PeasBlackberry

BeansCornOlive

GrapeKiwi slice

FishItalian dressing

Banana slicePeach slice

Cucumber slice

ChickenBalsamic dressing

CherryApple

SalmonPlum

Wholegrain breadBanana

Apple juiceOrange

Orange juiceWholegrain rollTomato sauce

Onion ringHerring

MustardLemon slice

Plain schnitzelQuark

Vanilla puddingCoffee

EspressoChocolate pudding

CappuccinoRaspberry quark

Herb saucePotatoes

Yoghurt dressingFeta

PastaRice

Bread rollCarbonara sauce

Chocolate marshmallowApple tart

French dressingFish stick

MuffinSugar

BratwurstCheese cakeMayonnaise

GravyBaguette slice

Burger pattyCrumbed schnitzel

ChocolatesChocolate cake

KetchupCoca-cola

Fries


Effects of the target person: Comparing behavioral standards for the self and peer. Interestingly,

comparing the meals chosen for the self versus for the peer revealed that even when choosing

unhealthy meals, healthy food items were chosen more often for individuals’ own meals (M = 4,

SE = 1.3) as compared to the peers’ meals (M = 2, SE = 1.7). Moreover, as shown in Figure 3,

unhealthy food items were chosen less often for the own meal (M = 16, SE = 1.7) as compared to the

peers’ meal (M = 20, SE = 2.2). For instance, only 12 participants chose Bratwurst for their own

unhealthy meal, whereas 31 participants chose Bratwurst for their peers’ unhealthy meal. Moreover,

when choosing typical meals, healthy food items were chosen more often for the own meal (M = 19,

SE = 2.0) as compared to the peers’ meal (M = 15, SE = 2.2). This was the case, for example, for

water, which 33 participants chose for their own typical meal, whereas only 10 participants chose

water for their peers’ typical meals. The remaining simple main effects of target were not significant.

The similarity of the typical to the healthy and unhealthy meals: A difference score analysis.

To further investigate these self-other discrepancies in behavioral standards, the similarity of the

typical meal to the healthy and unhealthy meal was examined using difference scores. The 2 × 2 × 3

ANOVA with the within-subject factors meal discrepancy (typical-healthy vs. typical-unhealthy) and

target (self vs. peer) and the between-subjects factor food category (healthy vs. neutral vs. unhealthy)

yielded a significant main effect of meal discrepancy and target (see Table 2). Simple main effect

analyses showed that participants’ typical meal was significantly more similar to their healthy meal

(MΔ = 3; SE = 0.8) than to their unhealthy meal (MΔ = 15; SE = 2.1) concerning healthy food items.

For instance, even more participants (n = 43) chose carrots for their typical meal than for their healthy

meal (n = 38). In contrast, only nine participants chose carrots for their unhealthy meal. The simple

main effect of meal discrepancy within the self was not significant for the neutral and unhealthy food

category. Interestingly, simple main effect analyses for the peers’ meals revealed that the peers’ typical

meals were not closer to the peers’ healthy meals than to the peers’ unhealthy meals concerning all

three food categories. Even more participants (n = 32) chose crumbed schnitzel, for example, for the

peers’ typical meal than for the peers’ unhealthy meal (n = 27). Moreover, the discrepancy between the

typical and healthy meal was significantly smaller for the own meal (MΔ = 3; SE = 0.8) as compared to

the peers’ meals (MΔ = 8; SE = 1.6) within the healthy food category. For instance, cauliflower was

chosen 26 times for participants’ own typical meal and 33 times for their healthy meal. In contrast,

cauliflower was chosen 14 times for the peers’ typical meals and 33 times for the peers’ healthy meals.

Hence, the discrepancy between typical and healthy was only seven for own meals but as high as 19

for the peers’ meals. The remaining simple main effects of target were not significant.


Table 2. Results of the ANOVAS with the number of participants who chose the food items as dependent variables.

Effect df Effect df Error F p η2p

3 × 2 × 3 ANOVA with the factors meal type (healthy vs. typical vs. unhealthy), target (self vs. peer), and food category (healthy vs. neutral vs. unhealthy)

Main effects and meal type × target interaction - - < 2.90 > 0.062 -

Meal type × food category interaction 2.60 86.93 30.48 < 0.001 0.48

Target × food category interaction 2 67 8.49 0.001 0.20

Three-way interaction 3.13 104.99 4.35 0.006 0.12

Simple contrasts within self

Healthy food category




Neutral food category 1 67 < 5.22 > 0.075 -

Unhealthy food category


Typical vs. unhealthy meal 1 67 11.71 0.003 0.15


Simple contrasts within peer





Neutral food category 1 67 < 2.17 > 0.146 -


Healthy vs. typical meal 1 67 12.16 0.003 0.15

Typical vs. unhealthy meal 1 67 11.03 0.003 0.14




Healthy meal 1 67 2.75 0.102 -


Table 2. Cont.

Typical meal 1 67 11.01 0.001 0.14

Unhealthy meal 1 67 5.58 0.021 0.08

Neutral food category < 0.35 > 0.555 -


Healthy meal 1 67 0.32 0.573 -

Typical meal 1 67 2.61 0.111 -

Unhealthy meal 1 67 11.67 0.001 0.15

2 × 2 × 3 ANOVA with the factor meal discrepancy (typical-healthy vs. typical-unhealthy), target (self vs. peer), and food category (healthy vs. neutral vs. unhealthy)

Main effect meal discrepancy 1 67 6.42 0.014 0.09

Main effect target 1 67 7.58 0.008 0.10

Main effect food category and all interactions < 2.21 > 0.118 -

Simple main effects meal discrepancy within self

Healthy food category 1 67 30.04 < 0.001 0.31

Neutral and unhealthy food category 1 67 < 2.68 > 0.106 -

Simple main effects meal discrepancy within peer

Healthy, neutral and unhealthy food category 1 67 < 2.05 > 0.157 -



Typical-healthy meal 1 67 17.91 < 0.001 0.21

Unhealthy-typical meal 1 67 2.54 0.115 -

Neutral and unhealthy food category 1 67 < 2.23 > 0.140 -


4. Discussion

The present study investigated self-other biases in actual eating behavior based on the observation

of behavioral standards for three different eating situations. To capture the diversity of food choices

available in real life within a well-controlled setting, an ecologically valid fake food buffet was

employed. The results showed that, when participants chose for themselves, a typical meal was more

similar to a healthy meal than to an unhealthy meal. Hence, one’s own typical behavior was more

similar to the healthy behavioral standard than to the unhealthy standard. Importantly, self-related meal

choices differed systematically from choices made for an average peer, indicating asymmetrical

behavioral standards for the peer and self. Specifically, participants chose less “healthy” meals for

others than for themselves both regarding quantity and quality, indicating a self-other discrepancy.

The results suggest that the observed self-peer discrepancy may be driven by a peer-bias which is

displayed in the less favorable assumed average behavior profiles as compared to the actual average

behavior profiles. In addition, the difference between the typical and the healthy behavioral standards

was smaller for the self than for others. These results indicate a comparatively positive self-view

which is in stark contrast to epidemiological data indicating that many people in the western world

eat unhealthily.

4.1. Behavioral Standards for Different Eating Situations

Overall, we found different behavioral standards between the three eating situations both for the self

and others. Interestingly, the typical behavioral standard for the self was relatively similar to the

standard for the healthy eating situation. Some healthy food items (e.g., carrots, corn) were even

chosen more often in the typical eating situation than in the healthy eating situation (see Figure 3). Our

results further show relative accuracy for the healthy and unhealthy meals considering the selection of

healthy and unhealthy foods. Consequently, the typical eating standard for the self was relatively

healthy. This finding might appear to be in contrast to headlines and studies stating that children and

adults have unhealthy eating habits [1,31,32]. However, there is evidence that overall unhealthy eating

patterns result from unhealthy snacking between meals rather than main meals [33].

Another interesting result is that the similarity of the typical and healthy eating standard for the self

was larger for healthy food items than for unhealthy food items. This might reflect that it is easier to

consume healthy foods than to suppress consuming unhealthy foods. Specifically, Wegner’s [34]

ironic process theory suggests that the suppression of a thought may ironically result in this thought

becoming more prevalent. For instance, Adriaanse and colleagues [35] showed that planning not to eat

unhealthy snacks ironically resulted in increased consumption.

The finding that the unhealthy and healthy eating standards were relatively accurate is in line with

previous research showing that people’s perception of what is healthy, in general, agrees with official

recommendations. For example, in several studies, people associated healthy eating with a high intake

of fruits and vegetables [36–39]. It is important to note, however, that the number of participants who

chose healthy food items for the healthy meal and unhealthy food items for the unhealthy meal varied

across food items. For instance, fries and Coca-Cola were very often chosen for the unhealthy meal,

whereas lettuce and tomatoes were very often chosen for the healthy meal (see Figure 3). In contrast,


this effect was less clear for other food items such as rice. These variations might, in part, reflect that

food items within the healthy and unhealthy category nevertheless differed in their nutritional quality

and that the categorization used in the present study is only one of several existing categorizations of

healthy and unhealthy foods. Moreover, stereotypes about unhealthy and healthy foods [40] might

account for the clear effect for fries, Coca-Cola, lettuce, and tomatoes.

4.2. Behavioral Standards for the Self as Compared to for Others

We found evidence for asymmetrical behavioral standards for the self as compared to for others

when it comes to a complex behavior, namely when choosing from 72 different food items. For

instance, there was a self-other difference of 1054 kJ (252 kcal) for a typical meal. The found

self-other bias is in line with previous findings of unrealistic optimism [9], false uniqueness [10], and

the holier-than-thou effect [11]. Furthermore, our results complement and extend previous research on

a self-favoring bias in self-reported healthy eating [2,3] and in the prediction of behaviors [11,14,16].

Specifically, our method allowed assessing separate behavior profiles for the self and others, which not

only illustrated a self-other bias at the group level but also shed light on the sources of the self-other

bias in actual behavior. Since the “assumed” average behavioral profile (aggregated food choices for

the average peer) was more negative than the “actual” average behavior profile (aggregated food

choices for the self), the observed self-other differences in our study can be attributed to a

pessimistically biased peer-perception (peer-bias) which results in a flattering self-perception

(see also [41]). As separate behavioral probes for the self and others were observed, prediction errors

due to ignoring situational constraints [16] can be excluded as a contributing factor to the observed

self-flattering bias. Moreover, observing actual behavior, rather than assessing self-reports of

hypothetical or future behavior, rules out a lack or neglect of self-information as an underlying

mechanism of the bias [5].

One potential mechanism for the observed peer-bias might be that others’ unhealthy eating behavior

is more salient than others’ healthy eating behavior. When people observe others eating, these eating

situations are often social. As research has shown that people eat more as well as more unhealthily [42]

in social as compared to solo eating situations, people’s peer-perception might rely more often

on social and thus, potentially more unhealthy eating situations. Moreover, people might engage in

self-enhancement [7,43] and attribute a less favorable behavioral standard for their peers, if their own

behavior is not in the desirable range. Conversely, if the own behavior is already in the desirable range

(i.e., the healthy eating situation), there might be no need for a behavioral social comparison standard

that is less favorable than the behavioral profile of the self. This motivational mechanism is in line

with our finding that, although people used different behavioral standards for the self than for others

within the typical and unhealthy eating situation, they did not within the healthy eating situation.

Interestingly, despite overall asymmetrical behavioral standards for the self and others in the typical

and unhealthy eating situation, “zooming” in on single food items revealed that the size of self-other

differences varied across food items. For instance, large differences in behavioral standards were found

for water and crumbed schnitzel within the typical eating situation. Water was chosen by 33 participants

for the own typical meal but only by 10 participants for the peers’ typical meal. Similarly, there was a

large difference for Bratwurst (self: n = 12, peer: n = 31) within the unhealthy eating situation.


In contrast, no difference occurred, for example, for chocolate marshmallow within the typical eating

situation (self: n = 0, peer: n = 0). This differentiated pattern speaks in favor of differently salient

consumption stereotypes. Specifically, research in the domain of risk perception has found larger

unrealistic optimism when a salient stereotype is in effect. In other words, people, on average, rated

their own risk for a hazard below that of an average person when they easily pictured a clear stereotype

of a typical victim of the hazard [9,44]. In line with this, a larger self-other bias in our study might

display that people find it easy to imagine a person who eats these specific unhealthy foods or avoids

these particular healthy foods. One conclusion from this is that public health education campaigns

might facilitate instead of reducing self-other biases in eating behavior (cf., [7]). Specifically, vivid

presentations of eaters who avoid healthy and consume unhealthy foods may foster consumption

stereotypes that are perceived as dissimilar. This might lead people to the conclusion that, although

their behavior is not optimal, they eat healthily compared to others.

The observed self-other discrepancy might also result in part from the fact that our sample had a

lower BMI than their average peer (M = 22.1 kg/m2 in our sample vs. M = 23.3 kg/m2 for an average

female between 20 and 25 years [45]). Hence, our sample might consume less energy and less

unhealthy foods than their average peers and further research is required to investigate self-other

discrepancies in a sample which is representative in terms of body mass index (BMI).

4.3. Limitations

Some limitations need to be taken into account. First, our results need to be interpreted with regard

to the sample of predominantly female, young adults with a normal BMI and high education from

Germany. Second, the fake food buffet lacked the smell of real foods, and the quantity of leftovers

could not be assessed. Third, our results provide information about the relative healthiness of meals but

not about whether the meals were healthy at an absolute level. The latter requires, for example,

investigation of the agreement of the meals with dietary guidelines such as MyPlate [46].

5. Conclusions

The present study uncovered behavioral standards for the self and others within three different

eating situations by employing an ecologically valid method to capture the complexity of real life food

choices. Participants’ behaviors indicate that they assume that others typically choose more energy and

more unhealthy food items than they do. This “I eat more healthily than you” perception seems to be

caused by a negative perception of the peer and, thus, needs to be extended to “I eat healthier than you

because you are eating so unhealthily” perception. This could explain why most people agree that

eating habits in the general population are unhealthy but think that they eat more healthily than others.

Acknowledgments

We would like to thank Rosemarie Brazdrum, Katrin Ziesemer, Bettina Ott, Tamara Bucher, and

Isabella Mauro for their valuable support in setting up the fake food method and conducting the study.

This work was supported by the Federal Ministry of Education and Research within the projects

EATMOTIVE (Grant 01EA1326, granted to Britta Renner & Harald Schupp) and SmartAct


(Grant 01EL1420A, granted to Britta Renner & Harald Schupp) as well as by the Committee on

Research (AFF) of the University of Konstanz (funding within the second funding period of the

Excellence Initiative of the German Federal and State Governments, granted to Britta Renner).

Publication costs are kindly covered by the DFG and open access publication funds of the University

of Konstanz.

Author Contributions

Britta Renner and Harald Schupp conceived and designed the study as well as provided materials.

Gudrun Sproesser and Verena Kohlbrenner participated in the generation of the study design.

Verena Kohlbrenner performed the study under the supervision of Gudrun Sproesser and

Britta Renner. Gudrun Sproesser and Verena Kohlbrenner analyzed the data. Britta Renner and

Harald Schupp contributed significantly to the interpretation of results. Gudrun Sproesser and

Britta Renner wrote the paper. Harald Schupp provided critical input. All authors approved the final

version of the manuscript for submission.

Appendix

Table A1. Details of the food items presented on the fake food buffet.

Food Item Real

Food (g)

Fake

Food

Fake

Food Unit

Energy Content Estimation

Method ª

Food

Category ᵇkJ/100 g kcal/100 g

Tomato slice 142 100 g 75 18 1 1

Lettuce 69 100 g 50 12 1 1

Water 200 1 200 mL Portion 0 0 1 1

Cucumber slice 92 100 g 50 12 1 1

Pepper slice 143 100 g 126 30 1 1

Carrots 130 100 g 138 33 1 1

Strawberry 18 1 Piece 134 32 1 1

Cauliflower 82 100 g 88 21 1 1

Peas 88 100 g 364 87 1 1

Blackberry 5 1 Piece 167 40 1 1

Beans 76 100 g 142 34 1 1

Corn 108 100 g 318 76 1 1

Olive 72 100 g 464 111 1 1

Grape 10 1 Piece 293 70 2 1

Kiwi slice 6 1 Piece 226 54 2 1

Fish 140 1 Piece 331 79 2 1


Table A1. Cont.

Italian dressing 44 1 50 ml Portion 1247 298 1 1

Banana slice 11 1 Piece 377 90 2 1

Peach slice 14 1 Piece 172 41 1 1

Chicken 82 100 g 481 115 1 1

Balsamic dressing 44 1 50 ml Portion 347 83 1 1

Cherry 13 1 Pair 251 60 1 1

Apple 140 1 Piece 255 61 2 1

Salmon 32 1 Slice 728 174 2 1

Plum 38 1 Piece 180 43 1 1

Wholegrain bread 31 1 Slice 828 198 2 1

Banana 126 1 Piece 377 90 2 1

Apple juice 200 1 200 ml Portion 197 47 1 1

Orange 168 1 Piece 180 43 2 1

Orange juice 200 1 200 mL Portion 180 43 1 1

Wholegrain roll 35 1 Piece 941 225 1 1

Tomato sauce 148 1 150 mL Portion 184 44 1 1

Onion ring 2 1 Piece 117 28 2 1

Herring 70 1 Slice 929 222 2 1

Mustard 10 1 10 mL Portion 360 86 1 1

Lemon slice 18 1 Piece 151 36 1 1

Plain schnitzel 125 1 Piece 849 203 2 2

Quark 250 1 250 mL Portion 305 73 1 2

Vanilla pudding 285 1 250 mL Portion 519 124 1 2

Coffee 300 1 300 mL Portion 8 2 1 2

Espresso 37 1 35 mL Portion 8 2 1 2

Chocolate pudding 285 1 250 mL Portion 397 95 1 2

Cappuccino 220 1 220 mL Portion 138 33 1 2

Raspberry quark 250 1 Piece 544 130 1 2

Herb sauce 150 1 150 mL Portion 406 97 1 2

Potatoes 113 100 g 297 71 1 2

Yoghurt dressing 50 1 50 mL Portion 607 145 1 2

Feta 110 100 g 1188 284 1 2

Pasta 87 100 g 582 139 1 3

Rice 110 100 g 527 126 1 3

Bread roll 22 1 Piece 1130 270 1 3

Carbonara sauce 150 1 150 mL Portion 1402 335 1 3

Chocolate marshmallow 23 1 Piece 1485 355 2 3

Apple tart 132 1 Piece 891 213 2 3

French dressing 50 1 50 mL Portion 1548 370 1 3

Fish stick 32 1 Piece 841 201 1 3

Muffin 37 1 Piece 1180 282 2 3

Sugar 4 1 Piece 1695 405 2 3

Bratwurst 140 1 Piece 1377 329 2 3

Cheese cake 100 1 Piece 1176 281 2 3

Mayonnaise 20 1 20 mL Portion 3109 743 1 3


Table A1. Cont.

Gravy 137 1 150 mL Portion 218 52 1 3

Baguette slice 24 1 Piece 1188 284 1 3

Burger patty 90 1 Piece 950 227 2 3

Crumbed schnitzel 120 1 Piece 950 227 2 3

Chocolate 10 1 Piece 1695 405 2 3

Chocolate cake 104 1 Piece 1611 385 2 3

Ketchup 20 1 20 mL Portion 460 110 1 3

Coca-Cola 200 1 200 mL Portion 197 47 1 3

Fries 82 100 g 1222 292 1 3

Salt, Pepper - 1 Portion - - - -

Note: a Estimation method of weight data: 1 indicates Comparison of fake food to real food, 2 indicates Data derived from [26]; b Food

categories were based on the German food pyramid [28]: 1 indicates Healthy foods, 2 indicates Neutral foods, 3 indicates

Unhealthy foods.

Conflicts of Interest

The authors declare no conflict of interest. The funding sources had no role in the design of the

study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the

decision to publish the results.

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